In this work we provide results of data mining and machine learning techniques, which form the basis of our prediction model for snail density classification in relation to the Schistosomiasis epidemic disease. All experiments to date are cognitive components in the development of our prediction model for the epidemic disease Schistosomiasis. This disease is detrimental to the health of the communities of affected areas as well as the crop and cattle life. If detected for early warning of the disease, the local communities can be better prepared to deal with any consequences of a breakout. This report gives an insight into the relationship between using a snapshot sample of environment data for epidemic disease vector classification, as opposed to the construction of an increased synthetic dataset.
|Title of host publication||Unknown Host Publication|
|Publisher||European Space Agency|
|Number of pages||1|
|Publication status||Published - 22 Jun 2015|
|Event||Dragon 3 symposium. ESA Communications. - |
Duration: 22 Jun 2015 → …
|Conference||Dragon 3 symposium. ESA Communications.|
|Period||22/06/15 → …|
- Earth observation
- Vector born disease
- Epidemic vector classification
Fusco, T., Bi, Y., Nugent, C. D., & Wu, S. (2015). A COMPARISON BETWEEN SYNTHETIC OVER- SAMPLING EQUILIBRIUM AND OBSERVED SUBSETS OF DATA FOR EPIDEMIC VECTOR CLASSIFICATION. In Unknown Host Publication European Space Agency.